{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as transforms\n",
    "from torchvision.transforms import RandAugment\n",
    "from torch.optim import lr_scheduler\n",
    "import numpy as np\n",
    "import time\n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from Resnet import *\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = ResNet().to(device)\n",
    "num_epochs=50\n",
    " # 定义损失函数和优化器\n",
    "trainloader,testloader = get_data_loader()\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 3.9721 Top-1 准确率: 0.0944 Top-5 准确率: 0.2837\n",
      "val 损失: 4.0899 Top-1 准确率: 0.1027 Top-5 准确率: 0.3139\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.2243 Top-1 准确率: 0.2057 Top-5 准确率: 0.4943\n",
      "val 损失: 3.5632 Top-1 准确率: 0.1862 Top-5 准确率: 0.4678\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 2.7353 Top-1 准确率: 0.2975 Top-5 准确率: 0.6188\n",
      "val 损失: 3.0987 Top-1 准确率: 0.2572 Top-5 准确率: 0.5582\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3905 Top-1 准确率: 0.3726 Top-5 准确率: 0.6959\n",
      "val 损失: 2.6759 Top-1 准确率: 0.3282 Top-5 准确率: 0.6527\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1468 Top-1 准确率: 0.4232 Top-5 准确率: 0.7487\n",
      "val 损失: 2.7088 Top-1 准确率: 0.3521 Top-5 准确率: 0.6640\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9752 Top-1 准确率: 0.4644 Top-5 准确率: 0.7814\n",
      "val 损失: 2.3798 Top-1 准确率: 0.4049 Top-5 准确率: 0.7170\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8268 Top-1 准确率: 0.4995 Top-5 准确率: 0.8068\n",
      "val 损失: 2.2974 Top-1 准确率: 0.4237 Top-5 准确率: 0.7406\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7091 Top-1 准确率: 0.5250 Top-5 准确率: 0.8284\n",
      "val 损失: 2.1193 Top-1 准确率: 0.4470 Top-5 准确率: 0.7716\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6128 Top-1 准确率: 0.5502 Top-5 准确率: 0.8441\n",
      "val 损失: 1.9975 Top-1 准确率: 0.4756 Top-5 准确率: 0.7888\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5301 Top-1 准确率: 0.5717 Top-5 准确率: 0.8575\n",
      "val 损失: 1.9090 Top-1 准确率: 0.5053 Top-5 准确率: 0.8005\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4540 Top-1 准确率: 0.5887 Top-5 准确率: 0.8683\n",
      "val 损失: 2.0716 Top-1 准确率: 0.4856 Top-5 准确率: 0.7915\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3771 Top-1 准确率: 0.6119 Top-5 准确率: 0.8808\n",
      "val 损失: 1.6435 Top-1 准确率: 0.5561 Top-5 准确率: 0.8427\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 1.3320 Top-1 准确率: 0.6228 Top-5 准确率: 0.8859\n",
      "val 损失: 1.7918 Top-1 准确率: 0.5388 Top-5 准确率: 0.8185\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2696 Top-1 准确率: 0.6390 Top-5 准确率: 0.8958\n",
      "val 损失: 1.8200 Top-1 准确率: 0.5141 Top-5 准确率: 0.8211\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 1.2205 Top-1 准确率: 0.6524 Top-5 准确率: 0.9022\n",
      "val 损失: 1.7212 Top-1 准确率: 0.5562 Top-5 准确率: 0.8356\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1787 Top-1 准确率: 0.6603 Top-5 准确率: 0.9079\n",
      "val 损失: 1.7796 Top-1 准确率: 0.5333 Top-5 准确率: 0.8137\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 1.1259 Top-1 准确率: 0.6775 Top-5 准确率: 0.9139\n",
      "val 损失: 1.4523 Top-1 准确率: 0.6044 Top-5 准确率: 0.8672\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0935 Top-1 准确率: 0.6826 Top-5 准确率: 0.9209\n",
      "val 损失: 1.6173 Top-1 准确率: 0.5718 Top-5 准确率: 0.8539\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0564 Top-1 准确率: 0.6929 Top-5 准确率: 0.9238\n",
      "val 损失: 1.6620 Top-1 准确率: 0.5713 Top-5 准确率: 0.8474\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 1.0181 Top-1 准确率: 0.7031 Top-5 准确率: 0.9289\n",
      "val 损失: 1.4525 Top-1 准确率: 0.6087 Top-5 准确率: 0.8706\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9853 Top-1 准确率: 0.7133 Top-5 准确率: 0.9329\n",
      "val 损失: 1.5144 Top-1 准确率: 0.5994 Top-5 准确率: 0.8612\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9490 Top-1 准确率: 0.7237 Top-5 准确率: 0.9371\n",
      "val 损失: 1.3741 Top-1 准确率: 0.6330 Top-5 准确率: 0.8812\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 0.9123 Top-1 准确率: 0.7335 Top-5 准确率: 0.9430\n",
      "val 损失: 1.5435 Top-1 准确率: 0.5927 Top-5 准确率: 0.8652\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8735 Top-1 准确率: 0.7431 Top-5 准确率: 0.9462\n",
      "val 损失: 1.5098 Top-1 准确率: 0.6023 Top-5 准确率: 0.8713\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8579 Top-1 准确率: 0.7472 Top-5 准确率: 0.9477\n",
      "val 损失: 1.6879 Top-1 准确率: 0.5754 Top-5 准确率: 0.8467\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 0.8368 Top-1 准确率: 0.7548 Top-5 准确率: 0.9495\n",
      "val 损失: 1.2489 Top-1 准确率: 0.6584 Top-5 准确率: 0.8980\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7996 Top-1 准确率: 0.7663 Top-5 准确率: 0.9536\n",
      "val 损失: 1.3961 Top-1 准确率: 0.6224 Top-5 准确率: 0.8808\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7751 Top-1 准确率: 0.7716 Top-5 准确率: 0.9564\n",
      "val 损失: 1.2907 Top-1 准确率: 0.6558 Top-5 准确率: 0.8918\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7649 Top-1 准确率: 0.7736 Top-5 准确率: 0.9595\n",
      "val 损失: 1.3363 Top-1 准确率: 0.6488 Top-5 准确率: 0.8928\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 0.7252 Top-1 准确率: 0.7854 Top-5 准确率: 0.9619\n",
      "val 损失: 1.5192 Top-1 准确率: 0.6108 Top-5 准确率: 0.8702\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6989 Top-1 准确率: 0.7935 Top-5 准确率: 0.9649\n",
      "val 损失: 1.5292 Top-1 准确率: 0.6130 Top-5 准确率: 0.8777\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6809 Top-1 准确率: 0.7979 Top-5 准确率: 0.9665\n",
      "val 损失: 1.5352 Top-1 准确率: 0.6082 Top-5 准确率: 0.8618\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6588 Top-1 准确率: 0.8064 Top-5 准确率: 0.9682\n",
      "val 损失: 1.4519 Top-1 准确率: 0.6223 Top-5 准确率: 0.8753\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6368 Top-1 准确率: 0.8107 Top-5 准确率: 0.9710\n",
      "val 损失: 1.3725 Top-1 准确率: 0.6441 Top-5 准确率: 0.8897\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 0.6142 Top-1 准确率: 0.8172 Top-5 准确率: 0.9723\n",
      "val 损失: 1.3978 Top-1 准确率: 0.6382 Top-5 准确率: 0.8908\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5958 Top-1 准确率: 0.8247 Top-5 准确率: 0.9743\n",
      "val 损失: 1.3290 Top-1 准确率: 0.6556 Top-5 准确率: 0.8938\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5769 Top-1 准确率: 0.8297 Top-5 准确率: 0.9747\n",
      "val 损失: 1.2823 Top-1 准确率: 0.6621 Top-5 准确率: 0.9011\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5584 Top-1 准确率: 0.8357 Top-5 准确率: 0.9763\n",
      "val 损失: 1.4736 Top-1 准确率: 0.6353 Top-5 准确率: 0.8758\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5364 Top-1 准确率: 0.8415 Top-5 准确率: 0.9788\n",
      "val 损失: 1.4631 Top-1 准确率: 0.6380 Top-5 准确率: 0.8778\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5188 Top-1 准确率: 0.8475 Top-5 准确率: 0.9811\n",
      "val 损失: 1.3764 Top-1 准确率: 0.6475 Top-5 准确率: 0.8875\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 0.5075 Top-1 准确率: 0.8510 Top-5 准确率: 0.9804\n",
      "val 损失: 1.3843 Top-1 准确率: 0.6553 Top-5 准确率: 0.8896\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4965 Top-1 准确率: 0.8529 Top-5 准确率: 0.9819\n",
      "val 损失: 1.2570 Top-1 准确率: 0.6736 Top-5 准确率: 0.9016\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4739 Top-1 准确率: 0.8620 Top-5 准确率: 0.9829\n",
      "val 损失: 1.2712 Top-1 准确率: 0.6753 Top-5 准确率: 0.8988\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4567 Top-1 准确率: 0.8684 Top-5 准确率: 0.9848\n",
      "val 损失: 1.2153 Top-1 准确率: 0.6813 Top-5 准确率: 0.9056\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4381 Top-1 准确率: 0.8735 Top-5 准确率: 0.9854\n",
      "val 损失: 1.2715 Top-1 准确率: 0.6709 Top-5 准确率: 0.8984\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4239 Top-1 准确率: 0.8765 Top-5 准确率: 0.9867\n",
      "val 损失: 1.3622 Top-1 准确率: 0.6622 Top-5 准确率: 0.8939\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4230 Top-1 准确率: 0.8781 Top-5 准确率: 0.9861\n",
      "val 损失: 1.3695 Top-1 准确率: 0.6580 Top-5 准确率: 0.8906\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 0.4080 Top-1 准确率: 0.8826 Top-5 准确率: 0.9876\n",
      "val 损失: 1.3144 Top-1 准确率: 0.6657 Top-5 准确率: 0.8946\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 0.3964 Top-1 准确率: 0.8872 Top-5 准确率: 0.9880\n",
      "val 损失: 1.3008 Top-1 准确率: 0.6732 Top-5 准确率: 0.9002\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 0.3919 Top-1 准确率: 0.8866 Top-5 准确率: 0.9887\n",
      "val 损失: 1.4987 Top-1 准确率: 0.6390 Top-5 准确率: 0.8812\n",
      "\n",
      "训练完成，耗时 111m 5s\n",
      "最佳验证集准确率: 0.681300\n"
     ]
    }
   ],
   "source": [
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  \n",
      "/root/deep-learning/FuTong/发起总攻/utils.py:79: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  \n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 39564 (\\N{CJK UNIFIED IDEOGRAPH-9A8C}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 35777 (\\N{CJK UNIFIED IDEOGRAPH-8BC1}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/root/miniconda3/lib/python3.10/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
